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Technology is a cornerstone of modern life, yet the software engineering field struggles to reflect the diversity of contemporary society. This lack of diversity and inclusivity within the software industry can be traced back to limited representation in software engineering academic settings, where students from underrepresented groups are often stigmatized despite the field's rich history of contributions from scientists from diverse backgrounds. Over the years, studies have revealed that women, LGBTQIA+ individuals, and Black students frequently encounter unwelcoming environments in software engineering programs. However, similar to other fields, increasing awareness of notable individuals from marginalized backgrounds could inspire students and foster a more inclusive environment. This study reports the findings from a replicated global survey with undergraduate software engineering students, exploring their knowledge of distinguished scientists from underrepresented groups. These findings show that students have limited awareness of these figures and their contributions, highlighting the need to improve diversity awareness and develop educational practices that celebrate the achievements of historically marginalized groups in software engineering. Index Terms-EDi in software engineering, software engineering education, diversity.

Software engineering (SE) faces significant diversity challenges in both academia and industry, with underrepresented students encountering hostile environments, limited representation, and systemic biases that hinder their academic and professional success. Despite significant research on the exclusion experienced by students from underrepresented groups in SE education, there is limited understanding of the specific motivations, influences, and role models that drive underrepresented students to pursue and persist in the field. This study explores the motivations and influences shaping the career aspirations of students from underrepresented groups in SE, and it investigates how role models and mentorship impact their decisions to stay in the field. We conducted a cross-sectional survey with undergraduate SE students and related fields, focusing on their motivations, influences, and the impact of mentorship and role models on their career paths. We identified eight motivations for pursuing SE, with career advancement, technological enthusiasm, and personal growth being the most common. Family members, tech influencers, teachers, and friends were key influences, though 64\% of students reported no specific individual influence. Role models, particularly tech influencers and family members play a critical role in sustaining interest in the field, especially for underrepresented groups. This study provides insights into the varied motivations and influences that guide underrepresented students' decisions to pursue SE. It emphasizes the importance of role models and highlights the need for intersectional approaches to better support diversity in the field.

[Context] Newcomers joining an unfamiliar software project face numerous barriers; therefore, effective onboarding is essential to help them engage with the team and develop the behaviors, attitudes, and skills needed to excel in their roles. However, onboarding can be a lengthy, costly, and error-prone process. Software solutions can help mitigate these barriers and streamline the process without overloading senior members. [Objective] This study aims to identify the state-of-the-art software solutions for onboarding newcomers. [Method] We conducted a systematic literature review (SLR) to answer six research questions. [Results] We analyzed 32 studies about software solutions for onboarding newcomers and yielded several key findings: (1) a range of strategies exists, with recommendation systems being the most prevalent; (2) most solutions are web-based; (3) solutions target a variety of onboarding aspects, with a focus on process; (4) many onboarding barriers remain unaddressed by existing solutions; (5) laboratory experiments are the most commonly used method for evaluating these solutions; and (6) diversity and inclusion aspects primarily address experience level. [Conclusion] We shed light on current technological support and identify research opportunities to develop more inclusive software solutions for onboarding. These insights may also guide practitioners in refining existing platforms and onboarding programs to promote smoother integration of newcomers into software projects.

Contributing to OSS projects can help students to enhance their skills and expand their professional networks. However, novice contributors often feel discouraged due to various barriers. Gamification techniques hold the potential to foster engagement and facilitate the learning process. Nevertheless, it is unknown which game elements are effective in this context. This study explores students' perceptions of gamification elements to inform the design of a gamified learning environment. We surveyed 115 students and segmented the analysis from three perspectives: (1) cognitive styles, (2) gender, and (3) ethnicity (Hispanic/LatinX and Non-Hispanic/LatinX). The results showed that Quest, Point, Stats, and Badge are favored elements, while competition and pressure-related are less preferred. Across cognitive styles (persona), gender, and ethnicity, we could not observe any statistical differences, except for Tim's GenderMag persona, which demonstrated a higher preference for storytelling. Conversely, Hispanic/LatinX participants showed a preference for the Choice element. These results can guide tool builders in designing effective gamified learning environments focused on the OSS contributions process.

Testing is an essential quality activity in the software development process. Usually, a software system is tested on several levels, starting with unit testing that checks the smallest parts of the code until acceptance testing, which is focused on the validations with the end-user. Historically, unit testing has been the domain of developers, who are responsible for ensuring the accuracy of their code. However, in agile environments, testing professionals play an integral role in various quality improvement initiatives throughout each development cycle. This paper explores the participation of test engineers in unit testing within an industrial context, employing a survey-based research methodology. Our findings demonstrate that testing professionals have the potential to strengthen unit testing by collaborating with developers to craft thorough test cases and fostering a culture of mutual learning and cooperation, ultimately contributing to increasing the overall quality of software projects.

A Large Language Model (LLM) represents a cutting-edge artificial intelligence model that generates coherent content, including grammatically precise sentences, human-like paragraphs, and syntactically accurate code snippets. LLMs can play a pivotal role in software development, including software testing. LLMs go beyond traditional roles such as requirement analysis and documentation and can support test case generation, making them valuable tools that significantly enhance testing practices within the field. Hence, we explore the practical application of LLMs in software testing within an industrial setting, focusing on their current use by professional testers. In this context, rather than relying on existing data, we conducted a cross-sectional survey and collected data within real working contexts, specifically, engaging with practitioners in industrial settings. We applied quantitative and qualitative techniques to analyze and synthesize our collected data. Our findings demonstrate that LLMs effectively enhance testing documents and significantly assist testing professionals in programming tasks like debugging and test case automation. LLMs can support individuals engaged in manual testing who need to code. However, it is crucial to emphasize that, at this early stage, software testing professionals should use LLMs with caution while well-defined methods and guidelines are being built for the secure adoption of these tools.

This article addresses the intersection of various educational theories and their relationship with the education of computer science students, with a focus on the importance of understanding computational thinking and its application in education. The historical context and fundamental concepts of Cognitive Load Theory, Multimedia Learning, and Constructivism are explored, highlighting their underlying biological assumptions about human learning. It also examines how these theories can be integrated with the use of Artificial Intelligence (AI) in education, with a particular emphasis on the attention mechanisms and abstract learning present in AI models like Transformers. Lastly, the relevance of these theories and practices for computer education student training is discussed, emphasizing how the development of computational thinking can contribute to a more effective approach in teaching and learning.

Labeling issues with the skills required to complete them can help contributors to choose tasks in Open Source Software projects. However, manually labeling issues is time-consuming and error-prone, and current automated approaches are mostly limited to classifying issues as bugs/non-bugs. We investigate the feasibility and relevance of automatically labeling issues with what we call "API-domains," which are high-level categories of APIs. Therefore, we posit that the APIs used in the source code affected by an issue can be a proxy for the type of skills (e.g., DB, security, UI) needed to work on the issue. We ran a user study (n=74) to assess API-domain labels' relevancy to potential contributors, leveraged the issues' descriptions and the project history to build prediction models, and validated the predictions with contributors (n=20) of the projects. Our results show that (i) newcomers to the project consider API-domain labels useful in choosing tasks, (ii) labels can be predicted with a precision of 84% and a recall of 78.6% on average, (iii) the results of the predictions reached up to 71.3% in precision and 52.5% in recall when training with a project and testing in another (transfer learning), and (iv) project contributors consider most of the predictions helpful in identifying needed skills. These findings suggest our approach can be applied in practice to automatically label issues, assisting developers in finding tasks that better match their skills., Comment: arXiv admin note: text overlap with arXiv:2103.12653

Social coding platforms such as GitHub have become defacto environments for collaborative programming and open source. When these platforms do not support specific cognitive styles, they create barriers to programming for some populations. Research shows that the cognitive styles typically favored by women are often unsupported, creating barriers to entry for woman newcomers. In this paper, we use the GenderMag method to evaluate GitHub to find cognitive style-specific inclusivity bugs. We redesigned the "buggy" GitHub features through a web browser plugin, which we evaluated through a between-subjects experiment (n=75). Our results indicate that the changes to the interface improve users' performance and self-efficacy, mainly for individuals with cognitive styles more common to women. Our results can inspire designers of social coding platforms and software engineering tools to produce more inclusive development environments.